Basketball can improve physical fitness, keep healthy, and become the main form of modern sports. However, basketball generally has the problem of unreasonable sports schemes and even reduces physical fitness. Therefore, finding an effective basketball program is an urgent problem to be solved at present. In order to improve the level of public health, this paper proposes a correlation analysis method to study the impact of basketball on teenagers’ health. The correlation analysis method is used to analyze the health data of adolescents, combined with the influence of basketball on fitness and the characteristics of basketball itself. Previous studies on basketball neglected comprehensive analysis and lack of correlation analysis of basketball indexes, resulting in unsatisfactory analysis results. The correlation analysis method can adjust the technical method, intensity, and program of basketball in combination with the physical development of teenagers. MATLAB simulation shows that the correlation analysis method can accurately analyze the impact of basketball on teenagers’ health, with an accuracy of 95%. Therefore, the correlation analysis method constructed in this paper can provide guidance for basketball and improve the health level of teenagers.

In order to improve the effect of intelligent teaching and give full play to the role of intelligent technology in modern physical education, in this paper, cloud computing and deep learning methods are used to comprehensively evaluate the teaching effect of colleges and universities, and calculate the evaluation effect and accuracy. Cloud computing and deep learning algorithm combine the teaching evaluation scale, teaching content, and characteristics to formulate teaching plans for different students and realize targeted teaching evaluation. The results show that the teaching evaluation method proposed in this paper can improve students’ learning interest by about 30%, enhance learning initiative by about 20%, and the matching rate between the actual teaching effect and the expected requirements is 98%. Therefore, cloud computing and deep learning model can improve the accuracy of teaching effect evaluation in colleges and universities, provide support for the formulation of teaching evaluation schemes, and promote the development of intelligent teaching in colleges and universities.

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In order to promote the development of sports industry teaching and accurately evaluate the teaching reform of sports industry, this paper constructs an evaluation model based on the teaching reform of sports industry. In addition, the k-means method is used to classify the teaching effect and simplify the data collection process, so as to improve the accuracy of teaching reform evaluation. The sample data come from the data released by the sports department and the government from 2017 to 2020, as well as the actual survey. The 26 evaluation indexes were determined by expert survey, questionnaire interview, and relevant domestic literature. In addition, the Euclidean distance in K-means method is used to calculate the weight of each index, and the results of the evaluation model are analyzed. The results show that the evaluation accuracy of the sports industry teaching reform model proposed in this paper is 98.4% and the error is 1.3%. The evaluation result is better than the previous ant colony model and is suitable for the evaluation of sports teaching reform.